Assignment 9 TV Shows on Streaming Platforms
Brianna Humphries
10/20/2020
Data Cleaning
library(readr)
#Data set found at:
#https://www.kaggle.com/ruchi798/tv-shows-on-netflix-prime-video-hulu-and-disney
#Updated May 25, 2020
#5611 total shows in data set
tvdata <- read_csv("tv_shows.csv", col_types = cols(X1 = col_skip(), type = col_skip(),
`Rotten Tomatoes` = col_character(),
Netflix = col_factor(levels = c("0", "1")),
Hulu = col_factor(levels = c("0", "1")),
`Prime Video` = col_factor(levels = c("0", "1")),
`Disney+` = col_factor(levels = c("0", "1")),
type = col_factor(levels = c("0", "1"))))## Warning: Missing column names filled in: 'X1' [1]#rename variables in data set for easier use
names(tvdata)[5] <- "RottenTomatoes"
names(tvdata)[8] <- "PrimeVideo"
names(tvdata)[9] <- "Disney"
#change variables to factor
tvdata$Year <- as.factor(tvdata$Year)
tvdata$Age <- as.factor(tvdata$Age)
#rename Age level all to All, and Platform levels to Yes or No
library(plyr)
tvdata$Age <- revalue(tvdata$Age, c("all"="All"))
tvdata$Netflix <- revalue(tvdata$Netflix, c("0"="No", "1"="Yes"))
tvdata$Hulu <- revalue(tvdata$Hulu, c("0"="No", "1"="Yes"))
tvdata$PrimeVideo <- revalue(tvdata$PrimeVideo, c("0"="No", "1"="Yes"))
tvdata$Disney <- revalue(tvdata$Disney, c("0"="No", "1"="Yes"))
#Order Age groups from young to old
tvdata$Age <- ordered(tvdata$Age, levels = c("All", "7+", "13+", "16+", "18+"))
#Remove % symbol from Rotten Tomatoes variable and change to numeric
tvdata$RottenTomatoes <- gsub("%", "", tvdata$RottenTomatoes)
tvdata$RottenTomatoes <- as.numeric(tvdata$RottenTomatoes)
#Show first 6 rows of dataset
head(tvdata)## # A tibble: 6 x 9
## Title Year Age IMDb RottenTomatoes Netflix Hulu PrimeVideo Disney
## <chr> <fct> <ord> <dbl> <dbl> <fct> <fct> <fct> <fct>
## 1 Breaking Bad 2008 18+ 9.5 96 Yes No No No
## 2 Stranger Thi… 2016 16+ 8.8 93 Yes No No No
## 3 Money Heist 2017 18+ 8.4 91 Yes No No No
## 4 Sherlock 2010 16+ 9.1 78 Yes No No No
## 5 Better Call … 2015 18+ 8.7 97 Yes No No No
## 6 The Office 2005 16+ 8.9 81 Yes No No Noattach(tvdata)Column Descriptions
Title: Title of TV show
Year: Year in which the TV show was released (1901-2020)
Age: Target Age Group (All, 7+, 13+, 16+, 18+)
IMDb: IMDb rating (0-10)
RottenTomatoes: Rotten Tomatoes percentage rating (0-100)
Netflix: Whether the TV show is found on Netflix (Yes or No)
Hulu: Whether the TV show is found on Hulu (Yes or No)
PrimeVideo: Whether the TV show is found on Prime Video (Yes or No)
Disney: Whether the TV show is found on Disney+ (Yes or No)
Summary of Data
#Show summary of data
summary(tvdata)## Title Year Age IMDb RottenTomatoes
## Length:5611 2017 : 653 All : 545 Min. :1.000 Min. : 6.0
## Class :character 2016 : 573 7+ : 848 1st Qu.:6.600 1st Qu.: 67.0
## Mode :character 2018 : 556 13+ : 4 Median :7.300 Median : 83.0
## 2015 : 454 16+ :1018 Mean :7.113 Mean : 77.5
## 2019 : 396 18+ : 750 3rd Qu.:7.900 3rd Qu.: 93.0
## 2014 : 373 NA's:2446 Max. :9.600 Max. :100.0
## (Other):2606 NA's :1161 NA's :4600
## Netflix Hulu PrimeVideo Disney
## No :3680 No :3857 No :3467 No :5431
## Yes:1931 Yes:1754 Yes:2144 Yes: 180
##
##
##
##
## Variable Distributions
Years
library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following objects are masked from 'package:plyr':
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## arrange, mutate, rename, summarise## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layout#Years
#There are no shows in years 1901-1903, 1905-1913, 1915-1930, 1933, 1935-1942, 1944, and 1946
#Shows every year from 1947-2020
#No missing values in data set
yearP <- plot_ly(tvdata, x = ~Year) %>%
add_histogram() %>%
layout(title = "Number of TV Shows per Year",
xaxis = list(title = "Year"),
yaxis = list(title = "Number of TV Shows"))
yearPAge Group
#Age
#2446 missing values in data set, so graph includes 3165 observations
ageNA <- tvdata[complete.cases(tvdata$Age),] #data set without missing age obs.
ageP <- plot_ly(ageNA, x = ~Age) %>%
add_histogram() %>%
layout(title = "Number of TV Shows per Age Group",
xaxis = list(title = "Age Group"),
yaxis = list(title = "Number of TV Shows"))
agePIMDb Rating
#IMDb rating
#1161 missing values in data, so graph includes 4450 observations
imdbNA <- tvdata[complete.cases(tvdata$IMDb),] #data set without missing IMDb
imdbP <- plot_ly(imdbNA, x = ~IMDb) %>%
add_histogram() %>%
layout(title = "Number of TV Shows per IMDb Rating",
xaxis = list(title = "IMDb Rating"),
yaxis = list(title = "Number of TV Shows"))
imdbP#591 tv shows with IMDb ratings less than 6 (13.3%)
sum(na.omit(IMDb) < 6)## [1] 591#3859 tv shows with IMDb ratings greater than or equal to 6 (86.7%)
sum(na.omit(IMDb) >= 6)## [1] 3859Rotten Tomatoes Percent Rating
#Rotten Tomatoes
#4600 missing values, so graph includes 1011 observations
rotNA <- tvdata[complete.cases(tvdata$RottenTomatoes),] #data set without NA RottenTomatoes
rotP <- plot_ly(rotNA, x = ~RottenTomatoes) %>%
add_histogram() %>%
layout(title = "Number of TV Shows per Rotten Tomatoes Percentage",
xaxis = list(title = "Rotten Tomatoes Percentage"),
yaxis = list(title = "Number of TV Shows"))
rotP#182 tv shows with IMDb ratings less than 6 (18.0%)
sum(na.omit(RottenTomatoes) < 60)## [1] 182#829 tv shows with IMDb ratings greater than or equal to 6 (82.0%)
sum(na.omit(RottenTomatoes) >= 60)## [1] 829Streaming Platforms
#number of tv shows in each platform
#No missing data in each platform
platMat <- sapply(X = tvdata[6:9], FUN = table) #frequency table
platforms <- c("Netflix", "Hulu", "Prime Video", "Disney+") #names of platforms
count_no <- platMat[1:1, 1:4] #extracts frequencies of "No" values
count_yes <- platMat[2:2, 1:4] #extracts frequencies of "Yes" values
count_yesN <- count_yes[1] #total number of shows in Netflix - 1931
count_yesH <- count_yes[2] #total number of shows in Hulu - 1754
count_yesP <- count_yes[3] #total number of shows in Prime Video - 2144
count_yesD <- count_yes[4] #total number of shows in Disney - 180
platdata <- data.frame(platforms, count_yes, count_no) #reorganization of freq table
#Plot number of tv shows in each platform as a grouped bar chart
plat <- plot_ly(platdata, x = ~platforms, y = ~count_yes, type = 'bar', name = 'Yes') %>%
add_trace(y = ~count_no, name = 'No') %>%
layout(title = "Number of TV Shows in Each Streaming Platform",
yaxis = list(title = 'Number of TV Shows'),
xaxis = list(title = 'Streaming Platform'),
barmode = 'group',
legend = list(title = list(text = "Is the show in the platform?")))
plat #plot platCorrelation between IMDb Rating and Rotten Tomatoes Percentage
#data set without missing rating variables
ratingNA <- tvdata[complete.cases(tvdata$RottenTomatoes, tvdata$IMDb),] #1008 observations
#fit <- lm(IMDb ~ RottenTomatoes, data=ratingNA) #for regression line
fit <- ratingNA %>% lm(RottenTomatoes ~ IMDb,.) %>% fitted.values
#Scatter plot of IMDb vs RottenTomatoes
ratePlot <- plot_ly(ratingNA, x = ~IMDb, y = ~RottenTomatoes, type="scatter", mode = "markers", text = ~paste("TV Show: ", Title, '<br>Year:', Year)) %>%
add_trace(x=~IMDb, y=fit, mode = "lines") %>%
layout(title = "IMDb Rating vs. Rotten Tomatoes Percentage",
xaxis = list(title = "IMDb Rating"),
yaxis = list(title = "Rotten Tomatoes Percentage"),
showlegend = FALSE)
ratePlot#Correlation test between IMDb and Rotten Tomatoes Ratings
cor.test(RottenTomatoes, IMDb) ##
## Pearson's product-moment correlation
##
## data: RottenTomatoes and IMDb
## t = 17.775, df = 1006, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4404173 0.5344820
## sample estimates:
## cor
## 0.4888694#p-value is less than 0.05, so correlation is significant
#Correlation coefficient: 0.489Platforms of “High Rating” TV Shows
#Create subset with top Rotten Tomatoes ratings from 80-100% or IMDb ratings from 8-10
#uses data without missing values since we are counting by platform
topRatings <- tvdata[which(tvdata$RottenTomatoes >= 80 | tvdata$IMDb >= 8.0),] #658 shows
#create dataset that includes the counts of the number of shows in or not in each platform with high ratings
platmatTop <- sapply(X = topRatings[6:9], FUN = table)
count_noTop <- platmatTop[1:1, 1:4]
count_yesTop <- platmatTop[2:2, 1:4]
platdataTop <- data.frame(platforms, count_yesTop, count_noTop)
#Plot of how many shows with high ratings are in or not in each platform
platTop <- plot_ly(platdataTop, x = ~platforms, y = ~count_yesTop, type = 'bar', name = 'Yes') %>%
add_trace(y = ~count_noTop, name = 'No') %>%
layout(title = "Number of TV Shows in Each Streaming Platform with High Ratings",
yaxis = list(title = 'Number of TV Shows'),
xaxis = list(title = 'Streaming Platform'),
barmode = 'group',
legend = list(title = list(text = "Is the show in the platform?")))
#Since data relies on there being a value in either IMDB or RottenTomatoes, then we need new count values that counts the tv shows in each platform when they don't have missings in both IMDb and RottenTomatoes
#Create dataset that includes values if not missing in both ratings
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
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## intersect, setdiff, setequal, unionratingNDNA <- full_join(imdbNA, rotNA) #4453 obs## Joining, by = c("Title", "Year", "Age", "IMDb", "RottenTomatoes", "Netflix", "Hulu", "PrimeVideo", "Disney")#count how many of each platform are in this dataset
platmatNRA <- sapply(X = ratingNDNA[6:9], FUN = table)
count_noNRA <- platmatNRA[1:1, 1:4]
count_yesNRA <- platmatNRA[2:2, 1:4]
platNA <- data.frame(platforms, count_yesNRA, count_noNRA) #reorganization of freq table
#Plot number of tv shows in each platform as a grouped bar chart
#for data without missing values in either IMDb or Rotten Tomatoes
#Shows that Netflix has more shows than Prime with this dataset
platNRA <- plot_ly(platNA, x = ~platforms, y = ~count_yesNRA, type = 'bar', name = 'Yes') %>%
add_trace(y = ~count_noNRA, name = 'No') %>%
layout(title = "Number of TV Shows in Each Streaming Platform (w/o missing)",
yaxis = list(title = 'Number of TV Shows'),
xaxis = list(title = 'Streaming Platform'),
barmode = 'group',
legend = list(title = list(text = "Is the show in the platform?")))
platNRA #plot platNRA#Calculate percent of tv shows that have a high rating
platTopPercY <- (count_yesTop / count_yesNRA)*100
platTopPercN <- (count_noTop / count_noNRA)*100
#Combine into percents and counts into dataset platdatatop
platdataTop <- cbind(platdataTop, platTopPercY)
platdataTop <- cbind(platdataTop, platTopPercN)
platdataTop <- cbind(platdataTop, count_yesNRA)
platdataTop <- cbind(platdataTop, count_noNRA)
#Percentage is of how many higher rating shows are in/not in the platform over the total number of shows in/not in the platform
#Ex: 33.4% of shows in Netflix have a high rating
#Ex: 30.7% of shows not in Prime Video have a high rating
platTopPercP <- plot_ly(platdataTop, x = ~platforms, y = ~platTopPercY, type = 'bar', name = 'Yes') %>%
add_trace(y = ~platTopPercN, name = 'No') %>%
layout(title = "Percent of TV Shows in Each Streaming Platform with High Ratings",
yaxis = list(title = 'Percent of TV Shows'),
xaxis = list(title = 'Streaming Platform'),
barmode = 'group',
legend = list(title = list(text = "Is the show in the platform?")))
platTop #plot top countsplatTopPercP #plot top percentsAge Groups of Platforms
library(purrr)##
## Attaching package: 'purrr'## The following object is masked from 'package:plyr':
##
## compact#Count how many shows in platforms when missing ages values are taken out
platMatAgeCount <- sapply(X = ageNA[6:9], FUN = table)
platforms <- c("Netflix", "Hulu", "Prime Video", "Disney+")
count_noA <- platMatAgeCount[1:1, 1:4]
count_yesA <- platMatAgeCount[2:2, 1:4]
count_yesAN <- count_yesA[1]
count_yesAH <- count_yesA[2]
count_yesAP <- count_yesA[3]
count_yesAD <- count_yesA[4]
#Create datasets for each platform with count levels
ageNetflix <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(Netflix, Age) %>% tally() %>% filter(Netflix == "Yes") %>% na.omit() %>% ungroup() %>% select(-Netflix) %>%
mutate(NetflixPer = (n / count_yesAN)*100)
names(ageNetflix)[2] <- "Netflix"
ageHulu <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(Hulu, Age) %>% tally() %>% filter(Hulu == "Yes") %>% na.omit() %>% ungroup() %>% select(-Hulu) %>%
mutate(HuluPer = (n / count_yesAH)*100)
names(ageHulu)[2] <- "Hulu"
agePrime <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(PrimeVideo, Age) %>% tally() %>% filter(PrimeVideo == "Yes") %>% na.omit() %>% ungroup() %>% select(-PrimeVideo) %>%
mutate(PrimePer = (n / count_yesAP)*100)
names(agePrime)[2] <- "PrimeVideo"
ageDisney <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(Disney, Age) %>% tally() %>% filter(Disney == "Yes") %>% na.omit() %>% ungroup() %>% select(-Disney) %>%
mutate(DisneyPer = (n / count_yesAD)*100)
names(ageDisney)[2] <- "Disney"
#Join datasets by Age and replace any null values to be 0
agePlatData <- full_join(ageNetflix, ageHulu, by="Age")
agePlatData <- full_join(agePlatData, agePrime, by="Age")
agePlatData <- full_join(agePlatData, ageDisney, by="Age")
agePlatData <- agePlatData %>% mutate_all(~replace(., is.na(.), 0))## Warning in `[<-.factor`(`*tmp*`, list, value = 0): invalid factor level, NA
## generated#create distribution of platforms for shows without missing ages
ageplatNA <- plot_ly(platNA, x = ~platforms, y = ~count_yesA, type = 'bar', name = 'Yes') %>%
add_trace(y = ~count_noA, name = 'No') %>%
layout(title = "Number of TV Shows in Each Streaming Platform (w/o missing)",
yaxis = list(title = 'Number of TV Shows'),
xaxis = list(title = 'Streaming Platform'),
barmode = 'group',
legend = list(title = list(text = "Is the show in the platform?")))
ageplatNA #plot#Create plot of count of ages by platforms
agePlat <- plot_ly(agePlatData, x = ~Age, y = ~Netflix, type = 'bar', name = 'Netflix', marker = list(color = "firebrick")) %>%
add_trace(y = ~Hulu, name = 'Hulu', marker = list(color = "#00EE76")) %>%
add_trace(y = ~PrimeVideo, name = 'Prime Video', marker = list(color = "#000033")) %>%
add_trace(y = ~Disney, name = 'Disney+', marker = list(color = "#0A47CC")) %>%
layout(title = "Number of Shows of Each Rating in Each Platform",
yaxis = list(title = 'Number Of Shows'),
xaxis = list(title = 'Age Group'),
barmode = 'group',
legend = list(title = list(text = "Platform")))
#Create plot of percent of ages by platforms
agePlatPer <- plot_ly(agePlatData, x = ~Age, y = ~NetflixPer, type = 'bar', name = 'Netflix', marker = list(color = "firebrick")) %>%
add_trace(y = ~HuluPer, name = 'Hulu', marker = list(color = "#00EE76")) %>%
add_trace(y = ~PrimePer, name = 'Prime Video', marker = list(color = "#000033")) %>%
add_trace(y = ~DisneyPer, name = 'Disney+', marker = list(color = "#0A47CC")) %>%
layout(title = "Percent of TV Shows in Each Age Group for Each Platform",
yaxis = list(title = 'Percent of TV Shows in Platform'),
xaxis = list(title = 'Age Group'),
barmode = 'group',
legend = list(title = list(text = "Platform")))
agePlatagePlatPerPlatforms Content by Release Year
#create data sets with counts of tv shows per year for each platform
yearNetflix <- tvdata %>% modify_if(is.character, as.factor) %>% group_by(Netflix, Year) %>% tally() %>% filter(Netflix == "Yes") %>% na.omit() %>% ungroup() %>% select(-Netflix) %>%
mutate(NetflixPer = (n / count_yesN)*100)
names(yearNetflix)[2] <- "Netflix"
yearHulu <- tvdata %>% modify_if(is.character, as.factor) %>% group_by(Hulu, Year) %>% tally() %>% filter(Hulu == "Yes") %>% na.omit() %>% ungroup() %>% select(-Hulu) %>%
mutate(HuluPer = (n / count_yesH)*100)
names(yearHulu)[2] <- "Hulu"
yearPrime <- tvdata %>% modify_if(is.character, as.factor) %>% group_by(PrimeVideo, Year) %>% tally() %>% filter(PrimeVideo == "Yes") %>% na.omit() %>% ungroup() %>% select(-PrimeVideo) %>%
mutate(PrimePer = (n / count_yesP)*100)
names(yearPrime)[2] <- "PrimeVideo"
yearDisney <- tvdata %>% modify_if(is.character, as.factor) %>% group_by(Disney, Year) %>% tally() %>% filter(Disney == "Yes") %>% na.omit() %>% ungroup() %>% select(-Disney) %>%
mutate(DisneyPer = (n / count_yesD)*100)
names(yearDisney)[2] <- "Disney"
#join datasets and replace NAs with 0
yearPlatData <- full_join(yearNetflix, yearHulu, by="Year")
yearPlatData <- full_join(yearPlatData, yearPrime, by="Year")
yearPlatData <- full_join(yearPlatData, yearDisney, by="Year")
yearPlatData <- yearPlatData %>% mutate_all(~replace(., is.na(.), 0))## Warning in `[<-.factor`(`*tmp*`, list, value = 0): invalid factor level, NA
## generatedyearPlatData$Year <- as.factor(yearPlatData$Year)
yearPlatData <- yearPlatData[order(yearPlatData$Year),] #order Years
#plot of show count per year per platform
yearPlatPlot <- plot_ly(yearPlatData, x = ~Year, y = ~Netflix, type = 'scatter', mode = "lines", name = 'Netflix', line = list(color = "firebrick")) %>%
add_trace(y = ~Hulu, name = 'Hulu', mode="lines", line = list(color = "#00EE76")) %>%
add_trace(y = ~PrimeVideo, name = 'Prime Video', mode="lines", line = list(color = "#000033")) %>%
add_trace(y = ~Disney, name = 'Disney+', mode="lines", line = list(color = "#0A47CC")) %>%
layout(title = "Each Platform's Content Available by Release Year",
yaxis = list(title = 'Number of TV Shows'),
xaxis = list(title = 'Year'),
legend = list(title = list(text = "Platform")))
yearPlatPlot#plot of show percent per year per platform
yearPlatPlotPer <- plot_ly(yearPlatData, x = ~Year, y = ~NetflixPer, type = 'scatter', mode = "lines", name = 'Netflix', line = list(color = "firebrick")) %>%
add_trace(y = ~HuluPer, name = 'Hulu', mode="lines", line = list(color = "#00EE76")) %>%
add_trace(y = ~PrimePer, name = 'Prime Video', mode="lines", line = list(color = "#000033")) %>%
add_trace(y = ~DisneyPer, name = 'Disney+', mode="lines", line = list(color = "#0A47CC")) %>%
layout(title = "Each Platform's Content Available by Release Year",
yaxis = list(title = 'Percent of TV Shows in Platform'),
xaxis = list(title = 'Year'),
legend = list(title = list(text = "Platform")))
yearPlatPlotPerRelation of Different Platforms
# create alluvial diagram
library(ggalluvial)
library(ggfittext)
Nalluv <- ggplot(tvdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) +
geom_alluvium(aes(fill=Netflix), knot.pos=0) +
geom_stratum(alpha=.5) +
geom_text(stat = "stratum", aes(label= after_stat(stratum))) +
scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
labs(title = "TV Shows of Streaming Platforms", subtitle = "stratified by Netflix",
y = "Frequency") +
theme_minimal()
Halluv <- ggplot(tvdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) +
geom_alluvium(aes(fill=Hulu), knot.pos=0) +
geom_stratum(alpha=.5) +
geom_text(stat = "stratum", aes(label= after_stat(stratum))) +
scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
labs(title = "TV Shows of Streaming Platforms", subtitle = "stratified by Hulu",
y = "Frequency") +
theme_minimal()
Palluv <- ggplot(tvdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) +
geom_alluvium(aes(fill=PrimeVideo), knot.pos=0) +
geom_stratum(alpha=.5) +
geom_text(stat = "stratum", aes(label= after_stat(stratum))) +
scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
labs(title = "TV Shows of Streaming Platforms", subtitle = "stratified by Prime Video",
y = "Frequency") +
theme_minimal()
Dalluv <- ggplot(tvdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) +
geom_alluvium(aes(fill=Disney), knot.pos=0) +
geom_stratum(alpha=.5) +
geom_text(stat = "stratum", aes(label= after_stat(stratum))) +
scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
labs(title = "TV Shows of Streaming Platforms", subtitle = "stratified by Disney+",
y = "Frequency") +
theme_minimal()
Nalluv## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
Halluv## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
Palluv## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
Dalluv## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
#There are no shows that are in all 4 platforms
plat4 <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "Yes"),]
#How many shows are on 3 platforms
#Not in Disney - 31
plat3NHP_D <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "No"),]
#Not in Hulu - 0
plat3N_HPD <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "No" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "Yes"),]
#Not in Netflix - 0
plat3_NHPD <- tvdata[which(tvdata$Netflix == "No" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "Yes"),]
#Not in Prime - 1
plat3NH_PD <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "No" & tvdata$Disney == "Yes"),]
head(plat3NH_PD)## # A tibble: 1 x 9
## Title Year Age IMDb RottenTomatoes Netflix Hulu PrimeVideo Disney
## <chr> <fct> <ord> <dbl> <dbl> <fct> <fct> <fct> <fct>
## 1 America's Fu… 1989 7+ 6.2 NA Yes Yes No Yes#Combine into 1 dataset - 32 (0.57% of data set)
plat3 <- rbind(plat3NHP_D, plat3NH_PD)
#How many shows are on 1 platforms
#Only in Netflix - 1748 - 90.5% of Netflix
plat1N <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "No" & tvdata$PrimeVideo == "No" & tvdata$Disney == "No"),]
#Only in Hulu - 1452 - 82.8% of Hulu
plat1H <- tvdata[which(tvdata$Netflix == "No" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "No" & tvdata$Disney == "No"),]
#Only in Prime - 1889 - 88.1% of Prime Video
plat1P <- tvdata[which(tvdata$Netflix == "No" & tvdata$Hulu == "No" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "No"),]
#Only in Disney - 156 - 87.7& of Disney+
plat1D <- tvdata[which(tvdata$Netflix == "No" & tvdata$Hulu == "No" & tvdata$PrimeVideo == "No" & tvdata$Disney == "Yes"),]
#Combine into 1 dataset - 5245 obs (93.5% of data set)
plat1 <- rbind(plat1N, plat1H, plat1P, plat1D)
#How many shows are on 2 platforms
#In Netflix and Hulu - 87
plat2NH <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "No" & tvdata$Disney == "No"),]
#In Netflix and Prime - 59
plat2NP <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "No" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "No"),]
#In Netflix and Disney - 5
plat2ND <- tvdata[which(tvdata$Netflix == "Yes" & tvdata$Hulu == "No" & tvdata$PrimeVideo == "No" & tvdata$Disney == "Yes"),]
#In Hulu and Prime - 165
plat2HP <- tvdata[which(tvdata$Netflix == "No" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "No"),]
#In Hulu and Disney - 18
plat2HD <- tvdata[which(tvdata$Netflix == "No" & tvdata$Hulu == "Yes" & tvdata$PrimeVideo == "No" & tvdata$Disney == "Yes"),]
#In Prime and Disney - 0
plat2PD <- tvdata[which(tvdata$Netflix == "No" & tvdata$Hulu == "No" & tvdata$PrimeVideo == "Yes" & tvdata$Disney == "Yes"),]
#Combine into 1 dataset - 334 obs (5.95% of data set)
plat2 <- rbind(plat2NH, plat2NP, plat2ND, plat2HP, plat2HD, plat2PD)